Automatic recognition of military vehicles with Krawtchouk moments

The challenge of Automatic Target Recognition (ATR) of military targets within a Synthetic Aperture Radar (SAR) scene is addressed in this paper. The proposed approach exploits the discrete defined Krawtchouk moments, that are able to represent a detected extended target with few features, allowing its characterization. The proposed algorithm provides robust performance for target recognition, identification and characterization, with high reliability in presence of noise and reduced sensitivity to discretization errors. The effectiveness of the proposed approach is demonstrated using the MSTAR dataset

Media gateway utilizando um GPU

Mestrado em Engenharia de Computadores e Telemátic

Processing of synthetic aperture radar data with GPGPU

Synthetic aperture radar processing is a complex task that involves advanced signal processing techniques and intense computational effort. While the first issue has now reached a mature stage, the question of how to produce accurately focused images in real-time, without mainframe facilities, is still under debate. The recent introduction of general-purpose graphic processing units seems to be quite promising in this view, especially for the decreased per-core cost barrier and for the affordable programming complexity. The authors explain, in this work, the main computational features of a range-Doppler Synthetic Aperture Radar (SAR) processor, trying to disclose the degree of parallelism in the operations at the light of the CUDA programming model. Given the extremely flexible structure of the Single Instruction Multiple Threads (SIMT) model, the authors show that the optimization of a SAR processing unit cannot reduce to an FFT optimization, although this is a quite extensively used kernel. Actually, it is noticeable that the most significant advantage is obtained in the range cell migration correction kernel where a complex interpolation stage is performed very efficiently exploiting the SIMT model. Performance show that, using a single Nvidia Tesla-C1060 GPU board, the obtained processing time is more than fifteen time better than our test workstation